Case Definitions of Clinical Malaria in Children from

Research ArticleCase Definitions of Clinical Malaria in Children fromThree Health Districts in the North Region of Cameroon

Raymond N Tabue 123 Boris A Njeambosay23 Francis Zeukeng 23 Livo F Esemu23

Barriegravere A Y Fodjo 23 Philomina Nyonglema2 Parfait Awono-Ambene4

Josiane Etang456 Etienne Fondjo1 Dorothy Achu1 Rose G F Leke2 Ceacutelestin Kouambeng1

Tessa B Knox7 Abraham P Mnzava8 and Jude D Bigoga 23

1Ministry of Public Health National Malaria Control Programme PO Box 14386 Yaounde Cameroon2National Reference Unit for Vector Control The Biotechnology Center University of Yaounde I PO Box 3851-MessaYaounde Cameroon

3Department of Biochemistry Faculty of Science University of Yaounde I PO Box 812 Yaounde Cameroon4Laboratoire de Recherche sur le Paludisme Institut de Recherche de YaoundeOrganisation de Coordination pour la lutte Contre les Endemies en Afrique Centrale PO Box 288 Yaounde Cameroon

5Faculty of Medicine and Pharmaceutical Sciences University of Douala PO Box 2701 Douala Cameroon6Institute for Insect Biotechnology Justus-Liebig-University Gieszligen Winchester Str 2 D-35394 Gieszligen Germany7Global Malaria Programme World Health Organization Geneva Switzerland8African Leaders Malaria Alliance (ALMA) Dar es Salaam Tanzania

Correspondence should be addressed to Jude D Bigoga judebigogayahoocom

Received 12 November 2018 Revised 26 February 2019 Accepted 20 March 2019 Published 16 April 2019

Academic Editor Stephen Munga

Copyright copy 2019 Raymond N Tabue et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Malaria endemicity in Cameroon greatly varies according to ecological environment In such conditions parasitaemia which isassociated with fever may not always suffice to define an episode of clinical malariaThe evaluation of malaria control interventionstrategiesmostly consists of identifying cases of clinicalmalaria and is crucial to promote better diagnosis for accuratemeasurementof the impact of the interventionWe sought out to define and quantify clinical malaria cases in children from three health districtsin the Northern region of Cameroon A cohort study of 6195 children aged between 6 and 120 months was carried out during theraining season (July to October) between 2013 and 2014 Differential diagnosis of clinical malaria was performed using the parasitedensity and axillary temperature At recruitment patients with malaria-related symptoms (fever [axillary temperature ge 375∘C]chills severe malaise headache or vomiting) and a malaria positive blood smear were classified under clinical malaria group Themalaria attributable fraction was calculated using logistic regression models Plasmodium falciparum was responsible for over 91of infections Children from Pitoa health district had the highest number of asymptomatic infections (4560) compared to thosefrom Garoua and Mayo Oulo The most suitable cut-off for the association between parasite densities and fever was found amongchildren less than 24 months Overall parasite densities that ranged above 3200 parasites per 120583l of blood could be used to definethe malaria attributable fever cases In groups of children aged between 24 and 59 months and 60 and 94 months the optimumcut-off parasite density was 6400 parasites per 120583l of blood while children aged between 95 and 120 months had a cut-off of 800parasites per 120583l of blood In the same ecoepidemiological zone clinical malaria case definitions are influenced by age and location(health district) and this could be considered when evaluating malaria intervention strategies in endemic areas

1 Introduction

Sub-Saharan African countries remain endemic for malariaand bear the highestmorbidity andmortality ratesThe risk of

havingmalaria is more pronounced in the Sahelian areas withfrequent outbreaks due to seasonality of the transmissionIn such areas individuals especially children often harbourmalaria parasites but without clinical symptoms [1]

HindawiBioMed Research InternationalVolume 2019 Article ID 9709013 8 pageshttpsdoiorg10115520199709013

2 BioMed Research International

N Northern region Health District1 - Mayo Oulo2 - Guider3 - Figuil4 - Gaschiga5 - Pitoa6 minus Garoua-7 - Bibeacutemi8 - Ngong9 - Lagdo10 - Rey-Bouba11 - Poli12 - Tchollireacute13 - Touboro

Figure 1 Map of the North Region of Cameroon showing the study health districts (1 5 and 6)

The policies recommend that in health facilities malariashould be first diagnosed by the presence of fever and asubsequent confirmatory blood test by microscopy or rapiddiagnostic test (RDT) However this is not always the casedue to many challenges such as delay in delivery of certainmaterials such as RDT or lack of laboratory technicianThese weaknesses limit malaria diagnoses to symptoms suchas fever only and could contribute to an overdiagnosis ofmalaria cases and consequently an inappropriate use ofantimalarial drugs Defining clinical malaria cases in suchendemic areas is difficult as clinical signs of the diseaseare nonspecific Moreover malaria parasitaemia which isassociated with clinical symptoms does not always implya situation of clinical malaria [2] Indeed individuals maycarry parasites without symptoms and coincidental febrileepisodes may have etiologies other than malaria Thereforeit is important to distinguish the disease caused by malariaparasites and the frequent asymptomatic infection causedby the same parasites This can be done throughout a clearcase definition of clinical malaria The use of a ldquopyrogenicthresholdrdquo has been proposed as the best alternative fordefining malaria disease in endemic areas [3 4] This vari-able depends however on the transmission intensity theseasonality of the disease the patientrsquos immunity and age InMozambique Smith et al 1998 [5] reported an age-specificmalaria attributable fraction (MAF) among children thatdecreased with increasing age This age-specific definition ofclinical malaria corroborates with results from other earlierstudies in highly endemic areas in sub-Saharan countries[5ndash7] MAF is also influenced by immune status In factimmunity is linked to transmission intensity and this varieswidely in many parts of Africa [8] Moreover variations overrelatively short distances can still influence clinical definitionsof malaria [2] For instance in the highlands of western

Kenya Afrane et al 2014 [9] showed a great variation ofMAF among people living in the valley bottom at the mid-hill and uphill

A clear and precise approach to estimate the proportionof fever attributable to malaria based on the model of therelationship between fever and parasite density has beenreported [1 10] This approach also allows the evaluationof the sensitivity and the specificity of the conventionalapproach in which cases are defined on the basis of feverand specific cut-off values for parasitaemia We thereforeassess the case definitions for clinical malaria in childrenaged between 6-120 months from three health districts in thesavannah areas of the Northern region of Cameroon by usingthe attributable fraction approach obtained from modellingthe relationship between fever risk and parasite densitySpecifically the study aimed to determine the estimatedmalaria-attributable fever in children by the estimation of thesensitivity and specificity of different parasite density cut-offpoints

2 Materials and Methods

21 Study Site This study was conducted from 2013 to2014 during the rainy season in three health districts (HD)in the North Region of Cameroon (Figure 1) Thirty-eightvillages were selected including 12 villages in Pitoa HD(9∘231015840010158401015840N 13∘ 321015840010158401015840E) located in a rice field area 17 villages inGaroua HD (9∘181015840010158401015840N 13∘241015840 010158401015840 E) located in periurban andurban areas and 09 villages in Mayo Oulo HD (9∘710158403410158401015840N13∘3710158402010158401015840E) located in the highlands The choice of selectedsites was guided by the existence of available entomologicaldata These selected HDs are characterized by a Sudaneseclimate type with an annual rainfall of 700-1000mmMalariatransmission here is seasonal with a peak of transmission

BioMed Research International 3

ranging from September to October Plasmodium falciparumis the most prevalent parasite species and malaria is mainlytransmitted by Anopheles arabiensis An gambiae and Anfunestus [11] Other species like An pharoensis andAn rufipesplay only secondary roles in the transmission of the disease[12 13] Agriculture is the prominent activity of the regionIrrigated rice fields and intensive cotton farming characterizePitoa InMayoOulo maize and peanuts aremainly cultivatedwhile corn tomatoes and eggplant are mainly grown inGaroua

22 Active Case Surveillance A cohort of over 6195 partic-ipants aged between 6-120 months was randomly selected(through a systematic random sampling) from over 1409houses in 38 villages In each health district houses wererandomly selected and all eligible children were enrolledprior to a written consent by their parents or a caregiverIn each village cohort details of all participants (parentrsquosname age sex ) were gathered A unique identification wasassigned to each participant and corresponded to the house-hold number and the village code Participants were visitedat home at least once every 2 weeks and information on thehealth of enrolled children was recorded and used to identifythose who may be having or have experienced fever withinthe last 48 hours or who they suspected to have malariaThe body axillary temperature was taken with a digitalthermometer and the symptoms and signs of the illness wererecorded Every child that was febrile or had been reportedto be febrile was tested (TDR) and labelled thin and thickblood smears were prepared Clinical cases were referred tothe nearest hospital for free treatment A clinical malaria casewas defined as an individual with malaria-related symptoms(fever [axillary temperature ge 375∘C] chills severe malaiseheadache or vomiting) at the time of examination or1ndash2 days prior to the examination and the presence of aPlasmodium positive blood smear Cohort members whodropped out were replaced All cases were entered intothe study database monthly The cohort was followed from2013 to 2014 during the raining season (July to October)Details of the methods used have previously been described[14]

23 Laboratory Slide Readings and Plasmodium Species Iden-tification Thin and thick blood smears were prepared andair-dried then the thin smears were fixed in methanol andstained in 4 Giemsa for 30 minutes Two independentmicroscopists examined the slides under a times100 oil immer-sionmicroscope objective to obtain a species-specific parasitecount Malaria parasites were counted against 200 whiteblood cells (WBC) and a blood smear was declared negativeafter examination of 100 high power fields The counts wereexpressed as the number of parasites per microliter of bloodassuming an average leukocyte count of 8000 cellsml ofblood For every slide where the difference in parasitaemiabetween the two readers was greater than five parasites permicroliter of blood a third reader had to re-examine theslide and the mean of the two closest values consideredFree malaria treatment with artesunate-amodiaquine wasgiven to all patients referred to the nearest health centre

in case of Plasmodium infection as recommended by thenational policy Plasmodium species were further confirmedby nested-PCR Briefly when a slide was positive for Plas-modium sp the portion of blood previously spotted on filterpapers (Whatman N∘3) was subjected to Plasmodium DNAextraction using the Chelex method [15] and the Plasmodiumspecies was identified by nested-PCR [16]

24 Ethical Considerations The Cameroon National EthicsCommittee (102CNESE09) gave ethical clearanceThe chil-dren were enrolled into the study if their parents or caregiverprovided a written consent after receiving an explanation ofthe study procedures

25 Malaria Case Definitions and Attributable Fraction Theattributable proportion of fever due to malaria was estimatedaccording to the method previously described by Smith [10]and which is based on a logistic regression of the fever ona monotonic function of the parasite density The logisticregression model used was

log it (120587i) = 120572 + 120573 (xi) 120591 (1)

Here (120587i) represents the probability that an observation (i)with parasite density xi is a fever case 120573(xi)120591 corresponds toa type 3 model described by Smith [10] and is a monotonicfunction which is more flexible than the regression on xi orlog(xi) The models were fitted using maximum likelihoodTo constrain the parameter 120591 to be positive the maximumlikelihood was estimated for the log(120591) Parasite density cut-off for different study areas and age groups was determinedby the intersection of sensitivity and specificity curves Themalaria attributable fraction was calculated as the reductionin the incidence that would be observed if the populationwas entirely unexposed compared with its current exposurepattern

26 Data Analysis Data were entered into Epi Info 353spread sheet processed and analysed using the same softwareSampled households were divided according to the healthdistrict Individuals who lived in the same health district werepooled together to calculate the prevalence of asymptomaticmalaria infections The asymptomatic malaria infection ratewas calculated as the ratio of infected individuals withoutsymptoms over total samples The differences in asymp-tomatic infection rates between districts were determinedby chi-square test Threshold for statistical significance wasset at Plt 005 Parasite thresholds were used to differentiateindividuals whose fever was caused by Plasmodium fromthose whose fever had other origin The sensitivity andspecificity of biological and molecular techniques appliedfor differential diagnosis were used to validate malaria casedefinition This was done by an estimation of parasite cut-offdensities in different settings using the point at which thesensitivity and specificity are both highest as the optimumcase definition The prevalence of the disease was used toestimate the fraction of prevalent febrile episodes that areattributable to the presence of malaria in blood


Table 1 Malaria indicators in children from the Northern region of Cameroon

Health districtsCharacteristics of malaria infection Garoua (n=2666) Pitoa (n=2026) Mayo Oulo (n=1503) Total (n=6195)Malaria infection prevalence () 2623 4783 1753 3115Gametocyte rate () 1216 73 1165 967Prevalence of fever () 183 227 153 19Fever + Plasmodium infection rate () 332 5468 30 4095Asymptomatic infection rate () 2461 456 1548 289Plasmodium species

P falciparum (Pf) () 9321 892 9161 9162P malariae (Pm) () 255 278 237 237Pf+Pm () 424 792 338 596Pf+Po () 0 01 0 005

n study population

3 Results

31 Prevalence of Fever and Asymptomatic Malaria ParasiteInfections The prevalence of fever due to malaria in childrenwas higher in Pitoa HD (227) compared to Garoua (183)and Mayo Oulo (153) although the difference was notsignificant (Chi-squared = 148 df = 2 p = 048) with anoverall prevalence of 19 The prevalence of fever casesassociated with Plasmodium infection was 4095 (Table 1)Among the recorded fever cases associated with Plasmodiuminfection was 3320 in Garoua 5468 in Pitoa and 30 inMayo Oulo The prevalence of fever related to infections var-ied significantly between investigated health districts (Chi-squared = 917 df = 2 p = 001)

Children living in Pitoa health district had the highestasymptomatic infection (4560) followed by those livingin Garoua (2461) and Mayo Oulo (1548) HDs (Chi-squared = 167 df = 2 P = 00002) The highest infection ratewas found in Pitoa (4783) compared to Garoua (2623)andMayo Oulo (1753) (Chi-squared = 044 df = 2 P = 08)Regarding the gametocytes index this was 1216 1165and 730 in Garoua Mayo Oulo and Pitoa respectively(Chi-squared = 12085 df = 2 Plt005) Plasmodium falci-parum was the most prevalent species in the three sites witha percentage of 9162 and was found in coinfections withP malariae at very low percentage (237) Multiple infectionsconsisted of the association of Pf+Pm (596) and Pf+Po(005) (Table 1)

32 Malaria Parasite Case Definition by Health District Thesensitivity and specificity of various cut-off parasite densitiesused in the definition of clinical malaria according to thehealth district are given in Figure 2 reproduced fromAfraneet al (2014) [9] [under the Creative Commons AttributionLicensepublic domain]

Overall the optimum cut-off parasite densities of 800 and6400 parasites per 120583l of blood could be suitable to definemalaria attributable cases in children from Mayo Oulo andPitoa Health districts respectively This cut-off was highestat 12800 parasites per 120583l of blood in individuals from theGaroua health district

33 Malaria Parasite Case Definitions by Age Malaria casedefinition by age was classified following the optimum cut-off parasite densities (Figure 3)The findings revealed that theparasite density of 3200 parasites per 120583l of blood could beused to define the malaria attributable fever cases in childrenless than 24 months of age In children aged 24-59 monthsand 60-94months the parasite density of 6400 parasites per120583l of blood is suitable to define the malaria attributable fevercases This cut-off decreased to 800 parasites per 120583l of bloodin children aged 95ndash120 months

34 Malaria Attributable Fraction Because the number offever cases encountered by health district was low thedetermination of the malaria attributable fraction (MAF) byage groups in each district was not possibleThus cumulativeMAFs by age in the three HDs were determined (Figure 3)From the model the mean malaria-attributable fractions orthe probability that any individual fever case was attributableto malaria were 011 023 027 and 034 in children aged95-120 months 60ndash94 months 24-59 months and less than24 months respectively Overall malaria-attributed fractiondecreased with age and varied significantly among investigat-ing health districts with 088 in Garoua 049 in Mayo Ouloand 019 in Pitoa (plt005) as observed in Table 2 reproducedfrom Afrane et al (2014) [9] [under the Creative CommonsAttribution Licensepublic domain]

4 Discussion

In endemic areas differential diagnosis of clinical malariacases is facing many challenges including the increasingprevalence of asymptomatic malaria infections and the non-specific symptoms of the disease Because the evaluation ofmalaria control interventions is mainly based on the mor-bidity trend in the population at risk differential diagnosisis critical for accurate impact intervention measures andfor first-line treatment administration [17] In most healthfacilities in African rural areas the lack of equipment anddiagnostic reagents for malaria in laboratories as well asqualified laboratory technicians or the disruption in malariaRDTs supply remains a serious technical issue to confirm or


PERC

ENTA

GE

80

100GAROUA HD PITOA HD

MAYO OULO HD

SensibilityPARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

Specificity

SensibilitySpecificity


60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40

Figure 2 Sensitivity and specificity values at given parasite density for different health districts

lt 24 MONTHS 24-59 MONTHS

60-94 MONTHS 95-120 MONTHS

PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40 PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40

SensitivitySpecificity




Figure 3 Sensitivity and specificity values at given parasite density for different age groups


Table 2 Malaria case definition parasite density cut-offs and fever attributable fraction of the cohort in Northern Cameroon

Cut-off values(parasites per 120583l of

blood)n fever n fever attributable

to malaria

malaria-attributablefraction s

Health DistrictsGaroua 12800 18 1588 088Pitoa 6400 23 428 019

Mayo Oulo 800 5 216 043

Age groups (months)

lt 24 3200 4 1354 03424-59 6400 18 4813 02760-94 6400 17 3949 02395-120 800 9 0979 011

discard clinical malaria cases Thus any fever case is oftenpresumed to be malaria when specific signs of other diseasesare lacking [18]

It is well documented that fever symptoms commonlyaccompany a wide range of childhood illnesses In generalyoung children are more vulnerable to infections due totheir immature immunity the different modes of exposure topathogenic organisms and possibly the low rate of immuni-sation [19] Among the 19 fever cases recorded in childrenonly 4095 were associated with Plasmodium infectionHence more than half of febrile children were estimated notto have malaria infection In the context of limited access toaffordable diagnostics and lack of knowledge of the causativeagents of other febrile illnesses most febrile cases are treatedand managed as malaria in malaria endemic areas [20]This inappropriate use of antimalarial drugs may lead topossible development of antimicrobial drug resistance andadditional treatment cost due to inappropriate prescriptionsIn our study area most infant fevers may be attributed toopportunistic bacterial or viral infections and might resultfrom an immune system being weakened by malnutritionIndeed about 70 of children under five years are highlysusceptible to severe acute malnutrition [21]

Our findings revealed that malaria is mesoendemic ininvestigating study sites with an overall prevalence of 3115This prevalence did not significantly vary by health districtAlthough the gametocytemia index was low the productionof gametocytes within an infected human host is essentialfor transmitting the parasite to the mosquito In a lowtransmission context the proportion of these sexual stagesand their density are seminal to understand the transmissionof malaria in the population [22] In the three health districtsPlasmodium falciparum was the most predominant malariaparasite species in investigating health districts as previouslydescribed [23 24] Although P vivax was absent its presencewas reported in the South part of the country [25 26]

The study revealed a significant difference in MAFsaccording to health districts This variation could be due to adifference in the level of transmission as previously describedelsewhere [9 27] Indeed malaria transmission in the NorthRegion of Cameroon is spatially and temporally driven byclimate variability In Garoua health district for instanceagricultural activities performed alongside the Benoue Riveras well as paddy fields in Pitoa health district create breeding

sites which are highly prominent for the reproduction ofvarious mosquito species [13] This heterogeneous environ-mental disturbance affects the distribution of larval breedinghabitats the spatial distribution of adult vector mosquitoesand thus could lead to different transmission intensity [13] Asexpected MAFs decreased while the children age increasedthis is likely due to the development of immunity

The differences observed in parasite density cut-off valuesamong children age groups could be attributed to the factthat older people can tolerate higher parasite densities thanyounger ones The immunity builds up during many yearspreferably after previous exposures and observed episodescould explain this [28 29] The parasite density cut-off valueto define clinical malaria cases was highest in individualsaged 24-59 months and 60-94 months thus showing towhat extent these children could tolerate malaria parasites intheir bodies without getting sick in comparison to childrenaged less than 24 months These findings underline howimmunity build-up affects asymptomatic parasite carriageUnexpectedly the cut-off values dropped to 800 parasites120583lamong children aged between 94 and 120 months Thus800 parasites per 120583l of blood were enough to cause malariain these children In contrary it is well known that olderchildren normally manage high parasite density because oftheir immune system which is well developed comparedto that of children under five years [27 30] The findingssuggest a shift in age groups at high risk of clinical episodesof malaria from children under five to older age groups andmay be related to the malaria control policy which is mainlyfocused on children aged under 5 years and pregnant womenA shift in malaria-affected age groups has also been reportedin Sub-Saharan African [31] This shift was associated withthe LLINs coverage In fact in some of these countries theacquisition of funding in 2004 from the Global Fund [32]scaled up malaria control interventions such as insecticide-treated nets (ITNs) It is suggested that focussing on childrenunder 5 years may cause a delay in the acquisition of naturalimmunity rendering these children at increased risk of acuteinfection once they have outgrown the age-specific controlinterventions at 5 years and older In the study area like allover the country LLINs are provided free of charge for chil-dren under 5 years and pregnant women [33] The nationalpolicy for malaria treatment recommends free antimalarialswith ACT (Artesunate + Amodiaquine) as first-line drug


for children under 5 years and intravenous Artesunate forpregnant women [34] The country has adopted universalLLINs coverage since 2010 and control interventions suchas free treatment strategy remain focused on children underfive as well as Seasonal Malaria Chemoprevention (SMC)These results suggest that in Northern Cameroon there isa considerable shift in the population at risk from childrenunder five years to older ones As the cut-off value for parasitedensity is low in older ages they could develop severemalariaTherefore there is a need to expand prevention strategiessuch as SMC to school-age children [35 36] to cover the peaktransmission months in order to impact more the diseaseburden

However the study faced some limits such as the factthat we were unable to determine the proportion of patientswhose fevers were treated with antipyretic drugs prior tothe visit In fact fever is the predominant symptom of theuse of self-medication by parents for the treatment of theirchildren in Cameroon [37] This may have influenced theproportion of fever recorded in the study population Alsothe malaria attributable fractions calculated from a singleblood-slide do not account for the parasitaemia that mayhave been undetected by the less sensitive optical microscopy[38] Furthermore the density of parasitaemia can fluctuatesignificantly within hours and therefore use of parasitaemiafrom a single blood film complicates the modelling [39]

5 Conclusion

In the Northern region of Cameroon where seasonal malariatransmission varies spatially clinical malaria should bedefined by age and by malaria transmission intensity whenevaluating interventions The high prevalence of nonpara-sitaemic fever underlies the fact that fever is not a specificmarker for clinical malaria hence there is a need to encour-age health workers to perform first-line diagnosis before theadministration of an antimalarial treatment The shift in agegroups at high risk of clinical episodes of malaria as observedfromchildren under five to older age groups suggests the needto expand prevention and free treatment strategies for olderchildren in order to cover the peak transmission months

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

We acknowledge the technical support of the NMCPOCEAC and the Biotechnology Center of University ofYaounde I We are indebted to the authorities of GarouaPitoa and Mayo Oulo Health Districts the population andparticipants for their collaboration This work is supported

by the Bill and Melinda Gates Foundation (Grant Number48499101) and World Health Organization

References

[1] J R M A Schellenberg T Smith P L Alonso and R J HayesldquoWhat is clinical malaria Finding case definitions for fieldresearch in highly endemic areasrdquo Parasitology Today vol 10no 11 pp 439ndash442 1994

[2] P Bejon T NWilliams A Liljander et al ldquoStable and unstablemalaria hotspots in longitudinal cohort studies in Kenyardquo PLoSMedicine vol 7 no 7 2010

[3] J F Trape P Peelman and B Morault-Peelman ldquoCriteria fordiagnosing clinical malaria among a semi-immune populationexposed to intense and perennial transmissionrdquo Transactions ofthe Royal Society of Tropical Medicine and Hygiene vol 79 no4 pp 435ndash442 1985

[4] BM Greenwood A K Bradley AM Greenwood et al ldquoMor-tality and morbidity from malaria among children in a ruralarea of The Gambia West Africardquo Transactions of the RoyalSociety of Tropical Medicine and Hygiene vol 81 no 3 pp 478ndash486 1987

[5] T Smith J D Charlwood A Y Kitua et al ldquoRelationships ofmalaria morbidity with exposure to Plasmodium falciparum inyoung children in a highly endemic areardquoTheAmerican Journalof Tropical Medicine and Hygiene vol 59 no 2 pp 252ndash2571998

[6] C Rogier D Commences and J-F Trape ldquoEvidence for anage-dependent pyrogenic threshold of Plasmodium falciparumparasitemia in highly endemic populationsrdquo The AmericanJournal of Tropical Medicine and Hygiene vol 54 no 6 pp 613ndash619 1996

[7] J K Baird T R Jones E W Danudirgo et al ldquoAge-dependentacquired protection against Plasmodium falciparum in peoplehaving two years exposure to hyperendemic malariardquo TheAmerican Journal of Tropical Medicine and Hygiene vol 45 no1 pp 65ndash76 1991

[8] S I Hay D J Rogers J F Toomer and R W Snow ldquoAnnualPlasmodium falciparum entomological inoculation rates (EIR)across Africa Literature survey internet access and reviewrdquoTransactions of the Royal Society of Tropical Medicine andHygiene vol 94 no 2 pp 113ndash126 2000

[9] Y A Afrane G Zhou A K Githeko and G Yan ldquoClinicalmalaria case definition and malaria attributable fraction in thehighlands of western Kenyardquo Malaria Journal vol 13 no 1 p405 2014

[10] T Smith J A Schellenberg and R Hayes ldquoAttributable fractionestimates and case definitions for malaria in endemic areasrdquoStatistics in Medicine vol 13 no 22 pp 2345ndash2358 1994

[11] C Antonio-Nkondjio CH Kerah F Simard et al ldquoComplexityof the malaria vectorial system in Cameroon Contribution ofsecondary vectors to malaria transmissionrdquo Journal of MedicalEntomology vol 43 no 6 pp 1215ndash1221 2006

[12] P H Awono-Ambene J Etang C Antonio-Nkondjio et alldquoThebionomics of themalaria vectorAnopheles rufipesGough1910 and its susceptibility to deltamethrin insecticide in NorthCameroonrdquo Parasites amp Vectors vol 11 no 1 p 253 2018

[13] R N Tabue P Awono-Ambene J Etang et al ldquoRole of Anophe-les (Cellia) rufipes (Gough 1910) and other local anophelinesin human malaria transmission in the northern savannah ofCameroon a cross-sectional surveyrdquo Parasites amp Vectors vol10 no 1 p 22 2017


[14] I Kleinschmidt A P Mnzava H T Kafy et al ldquoDesign ofa study to determine the impact of insecticide resistance onmalaria vector control a multi-country investigationrdquoMalariaJournal vol 14 no 1 p 282 2015

[15] C V Plowe A Djimde M Bouare O Doumbo and T EWellems ldquoPyrimethamine and proguanil resistance-conferringmutations in Plasmodium falciparum dihydrofolate reductasepolymerase chain reaction methods for surveillance in AfricardquoThe American Journal of Tropical Medicine and Hygiene vol 52no 6 pp 565ndash568 1995

[16] G Snounou S Viriyakosol W Jarra et al ldquoHigh sensitivity ofdetection of humanmalaria parasites by the use of nested poly-merase chain reactionrdquoMolecularandBiochemical Parasitologyvol 61 no 2 pp 315ndash320 1993

[17] C Roucher C Rogier F Dieye-Ba C Sokhna A Tall and J-FTrape ldquoChanging malaria epidemiology and diagnostic criteriafor plasmodium falciparum clinical malariardquo PLoS ONE vol 7no 9 2012

[18] R E Cibulskis M Aregawi R Williams M Otten and CDye ldquoWorldwide incidence of malaria in 2009 estimates timetrends and a critique of methodsrdquo PLoSMedicine vol 8 no 12Article ID e1001142 2011

[19] J L Hamilton and S P John ldquoEvaluation of fever in infants andyoung childrenrdquo American Family Physician vol 87 no 4 pp254ndash260 2013

[20] S Gwer C R J C Newton and J A Berkley ldquoOver-diagnosisand co-morbidity of severe malaria in african children a guidefor cliniciansrdquo The American Journal of Tropical Medicine andHygiene vol 77 no 6 pp 6ndash13 2007

[21] UNICEF UNICEF Humanitarian Action Update Sahel 2012httpwwwuniceforghac2012filesUNICEF HumanitarianAction Update - Sahel crisis - 6 February 2012pdf

[22] T Bousema and C Drakeley ldquoEpidemiology and infectivity ofplasmodium falciparum and plasmodium vivax gametocytes inrelation to malaria control and eliminationrdquoClinical Microbiol-ogy Reviews vol 24 no 2 pp 377ndash410 2011

[23] R Josse R Josseran M Audibert et al ldquoPaludometrie et varia-tions saisonnieres du paludisme dans la region du projet rizicolede Maga (Nord-Cameroun) et dans la region limitropherdquoCahiers Ostorm Serie EntomologieMedicale et Parasitologie vol25 pp 63ndash71 1987

[24] I A Quakyi R G F Leke R Befidi-Mengue et al ldquoThe epi-demiology of plasmodium falciparummalaria in two cameroo-nian villages simbok and etoardquo The American Journal ofTropical Medicine and Hygiene vol 63 no 5-6 pp 222ndash2302000

[25] J Fru-Cho V V Bumah I Safeukui T Nkuo-Akenji V P KTitanji and K Haldar ldquoMolecular typing reveals substantialPlasmodium vivax infection in asymptomatic adults in a ruralarea of CameroonrdquoMalaria Journal vol 13 no 1 p 170 2014

[26] H G N Mbenda and A Das ldquoMolecular evidence of plasmod-ium vivaxmono andmixedmalaria parasite infections in duffy-negative native camerooniansrdquo PLoS ONE vol 9 no 8 ArticleID e103262 2014

[27] A Dicko CMantel B Kouriba et al ldquoSeason fever prevalenceand pyrogenic threshold for malaria disease definition in anendemic area ofMalirdquoTropicalMedicineampInternational Healthvol 10 no 6 pp 550ndash556 2005

[28] D L Doolan C Dobano and J K Baird ldquoAcquired immunityto malariardquo Clinical Microbiology Reviews vol 22 no 1 pp 13ndash36 2009

[29] W J R Stone J J Campo A L Ouedraogo et al ldquoUnravellingthe immune signature of Plasmodium falciparum transmission-reducing immunityrdquo Nature Communications vol 9 no 1 p558 2018

[30] C Boudin V Robert P Carnevale and P Ambroise-ThomasldquoEpidemiology of Plasmodium falciparum in a rice field anda savanna area in Burkina Faso Comparative study on theacquired immunoprotection in native populationsrdquo Acta Trop-ica vol 51 no 2 pp 103ndash111 1992

[31] T Smith J L KHii B Genton et al ldquoAssociations of peak shiftsin age-prevalence for human malarias with bednet coveragerdquoTransactions of the Royal Society of Tropical Medicine andHygiene vol 95 no 1 pp 1ndash6 2001

[32] WHO Global Malaria Action Plan Roll Back Malaria Partner-ship Secretariat Geneva Switzerland 2008

[33] INS ldquoRapport MILDA 12 09 2013 - MILDA-Rapportrdquo httpwwwstatistics-cameroonorgdownloadsMILDAMILDA-Rap-port-completpdf

[34] NMCP ldquoNational Strategic Plan for Malaria Control ofCameroon 2011-2015rdquo 2011

[35] B Cisse E H Ba C Sokhna et al ldquoEffectiveness of seasonalmalaria chemoprevention in children under ten years of agein senegal a stepped-wedge cluster-randomised trialrdquo PLoSMedicine vol 13 no 11 Article ID e1002175 2016

[36] MAThera A K Kone B Tangara et al ldquoSchool-aged childrenbased seasonal malaria chemoprevention using artesunate-amodiaquine in Malirdquo Parasite Epidemiology and Control vol3 no 2 pp 96ndash105 2018

[37] C I Penda E C Eboumbou Moukoko J F Ngomba Youmbaand E M Mpondo ldquoCharacterization of pharmaceutical med-ication without a medical prescription in children beforehospitalization in a resource-limited setting Cameroonrdquo PanAfrican Medical Journal vol 30 2018

[38] L Ochola P Vounatsou T Smith M Mabaso and C NewtonldquoThe reliability of diagnostic techniques in the diagnosis andmanagement of malaria in the absence of a gold standardrdquoTheLancet Infectious Diseases vol 6 no 9 pp 582ndash588 2006

[39] K A Koram andM E Molyneux ldquoWhen is ldquomalariardquo malariaThe different burdens of malaria infection malaria diseaseand malaria-like illnessesrdquo The American Journal of TropicalMedicine and Hygiene vol 77 no 6 pp 1ndash5 2007

Hindawiwwwhindawicom

International Journal of

Volume 2018

Zoology

Hindawiwwwhindawicom Volume 2018

Anatomy Research International

PeptidesInternational Journal of



Journal of Parasitology Research

GenomicsInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018


BioinformaticsAdvances in

Marine BiologyJournal of



Neuroscience Journal


BioMed Research International

Cell BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawiwwwhindawicom Volume 2018


Genetics Research International


Advances in

Virolog y Stem Cells International



Enzyme Research



MicrobiologyHindawiwwwhindawicom

Nucleic AcidsJournal of

Volume 2018

Submit your manuscripts atwwwhindawicom


N Northern region Health District1 - Mayo Oulo2 - Guider3 - Figuil4 - Gaschiga5 - Pitoa6 minus Garoua-7 - Bibeacutemi8 - Ngong9 - Lagdo10 - Rey-Bouba11 - Poli12 - Tchollireacute13 - Touboro

Figure 1 Map of the North Region of Cameroon showing the study health districts (1 5 and 6)

The policies recommend that in health facilities malariashould be first diagnosed by the presence of fever and asubsequent confirmatory blood test by microscopy or rapiddiagnostic test (RDT) However this is not always the casedue to many challenges such as delay in delivery of certainmaterials such as RDT or lack of laboratory technicianThese weaknesses limit malaria diagnoses to symptoms suchas fever only and could contribute to an overdiagnosis ofmalaria cases and consequently an inappropriate use ofantimalarial drugs Defining clinical malaria cases in suchendemic areas is difficult as clinical signs of the diseaseare nonspecific Moreover malaria parasitaemia which isassociated with clinical symptoms does not always implya situation of clinical malaria [2] Indeed individuals maycarry parasites without symptoms and coincidental febrileepisodes may have etiologies other than malaria Thereforeit is important to distinguish the disease caused by malariaparasites and the frequent asymptomatic infection causedby the same parasites This can be done throughout a clearcase definition of clinical malaria The use of a ldquopyrogenicthresholdrdquo has been proposed as the best alternative fordefining malaria disease in endemic areas [3 4] This vari-able depends however on the transmission intensity theseasonality of the disease the patientrsquos immunity and age InMozambique Smith et al 1998 [5] reported an age-specificmalaria attributable fraction (MAF) among children thatdecreased with increasing age This age-specific definition ofclinical malaria corroborates with results from other earlierstudies in highly endemic areas in sub-Saharan countries[5ndash7] MAF is also influenced by immune status In factimmunity is linked to transmission intensity and this varieswidely in many parts of Africa [8] Moreover variations overrelatively short distances can still influence clinical definitionsof malaria [2] For instance in the highlands of western

Kenya Afrane et al 2014 [9] showed a great variation ofMAF among people living in the valley bottom at the mid-hill and uphill

A clear and precise approach to estimate the proportionof fever attributable to malaria based on the model of therelationship between fever and parasite density has beenreported [1 10] This approach also allows the evaluationof the sensitivity and the specificity of the conventionalapproach in which cases are defined on the basis of feverand specific cut-off values for parasitaemia We thereforeassess the case definitions for clinical malaria in childrenaged between 6-120 months from three health districts in thesavannah areas of the Northern region of Cameroon by usingthe attributable fraction approach obtained from modellingthe relationship between fever risk and parasite densitySpecifically the study aimed to determine the estimatedmalaria-attributable fever in children by the estimation of thesensitivity and specificity of different parasite density cut-offpoints

2 Materials and Methods

21 Study Site This study was conducted from 2013 to2014 during the rainy season in three health districts (HD)in the North Region of Cameroon (Figure 1) Thirty-eightvillages were selected including 12 villages in Pitoa HD(9∘231015840010158401015840N 13∘ 321015840010158401015840E) located in a rice field area 17 villages inGaroua HD (9∘181015840010158401015840N 13∘241015840 010158401015840 E) located in periurban andurban areas and 09 villages in Mayo Oulo HD (9∘710158403410158401015840N13∘3710158402010158401015840E) located in the highlands The choice of selectedsites was guided by the existence of available entomologicaldata These selected HDs are characterized by a Sudaneseclimate type with an annual rainfall of 700-1000mmMalariatransmission here is seasonal with a peak of transmission








log it (120587i) = 120572 + 120573 (xi) 120591 (1)







n study population

3 Results







4 Discussion



PERC

ENTA

GE

80


MAYO OULO HD


040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

Specificity



60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40




PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40 PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40










to malaria



Mayo Oulo 800 5 216 043

Age groups (months)

lt 24 3200 4 1354 03424-59 6400 18 4813 02760-94 6400 17 3949 02395-120 800 9 0979 011










5 Conclusion


Data Availability




Acknowledgments



References











































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018









log it (120587i) = 120572 + 120573 (xi) 120591 (1)







n study population

3 Results







4 Discussion



PERC

ENTA

GE

80


MAYO OULO HD


040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

Specificity



60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40




PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40 PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40










to malaria



Mayo Oulo 800 5 216 043

Age groups (months)

lt 24 3200 4 1354 03424-59 6400 18 4813 02760-94 6400 17 3949 02395-120 800 9 0979 011










5 Conclusion


Data Availability




Acknowledgments



References











































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018






n study population

3 Results







4 Discussion



PERC

ENTA

GE

80


MAYO OULO HD


040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

Specificity



60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40




PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40 PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40










to malaria



Mayo Oulo 800 5 216 043

Age groups (months)

lt 24 3200 4 1354 03424-59 6400 18 4813 02760-94 6400 17 3949 02395-120 800 9 0979 011










5 Conclusion


Data Availability




Acknowledgments



References











































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018



PERC

ENTA

GE

80


MAYO OULO HD


040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

Specificity



60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40




PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE

80

100

PARASITE DENSITY

60

40

PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40 PERC

ENTA

GE 80

100

PARASITE DENSITY

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

040

080

016

0032

0064

0012

800

2560

0

gt 2560

0

60

20

40










to malaria



Mayo Oulo 800 5 216 043

Age groups (months)

lt 24 3200 4 1354 03424-59 6400 18 4813 02760-94 6400 17 3949 02395-120 800 9 0979 011










5 Conclusion


Data Availability




Acknowledgments



References











































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018






to malaria



Mayo Oulo 800 5 216 043

Age groups (months)

lt 24 3200 4 1354 03424-59 6400 18 4813 02760-94 6400 17 3949 02395-120 800 9 0979 011










5 Conclusion


Data Availability




Acknowledgments



References











































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018





5 Conclusion


Data Availability




Acknowledgments



References











































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018




Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018


Documents

Case Definitions of Clinical Malaria in Children from